In recent years, the idea of sim-to-real policy adaptation has become a promising alternative to achieve data-efficiency in robot learning using RL [1]}, [2]}. In this approach, first, a policy is learned in the simulation. Then, that policy is adapted through real-world interactions to cross the reality-gap, the unmodeled or unknown variations between the simulation and the reality.
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